
'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_UpperCAmelCase :   Tuple			    =	logging.get_logger(__name__)

_UpperCAmelCase :   Any			    =	{'vocab_file': 'vocab.txt'}

_UpperCAmelCase :   List[Any]			    =	{
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

_UpperCAmelCase :   str			    =	{
    'facebook/esm2_t6_8M_UR50D': 1024,
    'facebook/esm2_t12_35M_UR50D': 1024,
}







def     snake_case__						(   UpperCamelCase						)			->   Optional[Any]:
		with open(_A      ,'''r'''						) as f:
				_UpperCamelCase							:			Union[str, Any]     =   f.read().splitlines()
				return [l.strip() for l in lines]


class       UpperCAmelCase					(       lowerCamelCase__			):





		"""simple docstring"""





		A__	: Any    =     VOCAB_FILES_NAMES
		A__	: List[Any]    =     PRETRAINED_VOCAB_FILES_MAP
		A__	: Dict    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A__	: Optional[int]    =     ['input_ids', 'attention_mask']
		def __init__(     self	,       _snake_case	,       _snake_case="<unk>"	,       _snake_case="<cls>"	,       _snake_case="<pad>"	,       _snake_case="<mask>"	,       _snake_case="<eos>"	,       **_snake_case	,       )					->     List[str]:
				super().__init__(**__snake_case  )
				_UpperCamelCase							:			Union[str, Any]     =   load_vocab_file(__snake_case  )
				_UpperCamelCase							:			int     =   dict(enumerate(self.all_tokens  )  )
				_UpperCamelCase							:			Optional[int]     =   {tok: ind for ind, tok in enumerate(self.all_tokens  )}
				_UpperCamelCase							:			Any     =   unk_token
				_UpperCamelCase							:			Union[str, Any]     =   cls_token
				_UpperCamelCase							:			Any     =   pad_token
				_UpperCamelCase							:			Tuple     =   mask_token
				_UpperCamelCase							:			Tuple     =   eos_token
				_UpperCamelCase							:			Optional[int]     =   self.all_tokens
				self._create_trie(self.unique_no_split_tokens  )
		def _lowercase	(     self	,       _snake_case  )					->     Dict:
				return self._id_to_token.get(__snake_case	,       self.unk_token  )
		def _lowercase	(     self	,       _snake_case  )					->     List[Any]:
				return self._token_to_id.get(__snake_case	,       self._token_to_id.get(self.unk_token  )  )
		def _lowercase	(     self	,       _snake_case	,       **_snake_case  )					->     Optional[int]:
				return text.split()
		def _lowercase	(     self	,       _snake_case=False  )					->     List[str]:
				return len(self._id_to_token  )
		def _lowercase	(     self  )					->     List[str]:
				return {token: i for i, token in enumerate(self.all_tokens  )}
		def _lowercase	(     self	,       _snake_case  )					->     int:
				return self._token_to_id.get(__snake_case	,       self._token_to_id.get(self.unk_token  )  )
		def _lowercase	(     self	,       _snake_case  )					->     str:
				return self._id_to_token.get(__snake_case	,       self.unk_token  )
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None  )					->     str:
				_UpperCamelCase							:			Dict     =   [self.cls_token_id]
				_UpperCamelCase							:			Dict     =   [self.eos_token_id]  # No sep token in ESM vocabulary
				if token_ids_a is None:
						if self.eos_token_id is None:
								return cls + token_ids_a
						else:
								return cls + token_ids_a + sep
				elif self.eos_token_id is None:
						raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''  )
				return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None	,       _snake_case = False  )					->     Optional[Any]:
				if already_has_special_tokens:
						if token_ids_a is not None:
								raise ValueError(
								    '''You should not supply a second sequence if the provided sequence of '''
								    '''ids is already formatted with special tokens for the model.'''  )

						return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
				_UpperCamelCase							:			List[str]     =   [1] + ([0] * len(__snake_case  )) + [1]
				if token_ids_a is not None:
						mask += [0] * len(__snake_case  ) + [1]
				return mask
		def _lowercase	(     self	,       _snake_case	,       _snake_case  )					->     Optional[Any]:
				_UpperCamelCase							:			Optional[int]     =   os.path.join(__snake_case	,       (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''  )
				with open(__snake_case	,       '''w'''  ) as f:
						f.write('''\n'''.join(self.all_tokens  )  )
				return (vocab_file,)
		@property
		def _lowercase	(     self  )					->     Dict:
				return self.get_vocab_size(with_added_tokens=__snake_case  )




		def _lowercase	(     self	,       _snake_case	,       _snake_case = False  )					->     Any:
				return super()._add_tokens(__snake_case	,       special_tokens=__snake_case  )










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)